Research Group for Applied Software Engineering
Forschungsgruppe für Angewandte Softwaretechnik

Benjamin Taheri                                                                                                       

PhD in computer science; researched in computer vision applications in industrial overhaul processes.

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Technische Universität München
Institut für Informatik I1
Boltzmannstraße 3
85748 Garching b. München, Germany

 

https://wwwbruegge.in.tum.de/lehrstuhl_1/images/linkedin.jpg

 BenjaminTaheri

 

 

 

 

 

 Office Hours:

  By appointment. Please contact me via email. Not working at TUM anymore.

 Research Interests:

 Publications:

 Teaching:

 Projects:

 Theses and Guided Researches:

 Please find underneath the offered, in progress and finished topics. Generally, I work on computer vision and machine learning applications in overhaul processes. It covers different industrial use cases like image classification, object detection and segmentation, damage identification and action recognition.

Offered

  No offers at the moment.

In Progress

                     

Finished

 

Master's Thesis
Sorting Fasteners based on their Similarity using Siamese Networks
Advisor
Sajjad Taheri
Author
Haonan Yu
Date
15.09.2019
The scope of this master thesis is to build an (semi)automatic system to train a Siamese net to find the similarity of the fasteners (namely bolts, nuts, screws and washers). Using the output of the Siamese net, the system should group and sort the fasteners in similar groups (in a fine-grained manner. For example it should put all the AS1800 bolts in one group and all the MS2650 bolts in another one). To successfully undertake this thesis, you need to have solid knowledge on machine learning and convolutional neural networks, together with experience in Python programming and working with at least one deep learning framework (Keras, PyTorch, TensorFlow).
 
Master's Thesis
Action Recognition Framework in Overhauling Processes
Advisor
Sajjad Taheri
Author
Valon Xhafa
Date
15.10.2018
An expert level know-how is considered as one of the most important factors in dealing with overhauling processes. What has been already done and what has to be done now? The aim of this master thesis is to be able to understand the process, the already done tasks and still to-do tasks using the computer vision and machine learning based approaches. The challenge is to come up with a proper representation of the whole process to the system that helps us to fetch the needed information.
 
Bachelor's Thesis
Getting Depth Image of Small Parts Using Multi-Cameras
Advisor
Sajjad Taheri
Author
Paul Rangger
Date
15.10.2018
Since the small parts and fasteners are scale-variant (which means that with scaling them we'll end up having another small part), classification of them needs a fixed camera to ensure that the distance between the camera lens and the object is always fixed. However, using two or more cameras, we are able to get the depth information of the objects, including their size. In this bachelor thesis, you will work on getting these depth images, using two or more cameras. The true challenge is to consider the small parts characteristics (their small size and their shiny surface) and find solutions to handle them.
            
Bachelor's Thesis
Classification of Diatoms Using Convolutional Neural Networks
Advisor
Sajjad Taheri
Author
Bettina Heigl
Date
15.04.2018
Creating dataset for the diatoms (small algae which can be found in all waters), train a model using convolutional neural nets to classify them, and compare the results with the traditional methods.
    
Master's Thesis
A Multi-view CNN Approach to Classify Bolts and Nuts in Overhauling Processes
Advisor
Sajjad Taheri
Author
René Svartdal Birkeland
Date
15.05.2018
A new approach to create datasets for classification of different nuts, considering the inner threads and camera angle. The challenge is to preserve the size information of the small part, using a fixed camera and fixed distance to that, while pointing out the camera lens to the nut in specific angles that it can capture its threads, length and overall shape. Using this setup and also other pictures, you will train a model with convolutional neural network to classify different nuts.
  
Master's Thesis
Automatic Detection of Damaged Small Parts during Overhauling Processes
Advisor
Sajjad Taheri
Author
Ralf Schönfeld
Date
15.04.2018
Creating a model to detect damaged small parts (screws, bolts, nuts, pins, washers, etc.) in overhauling industrial machineries, using deep learning and convolutinal neural networks
  
Master's Thesis
Using Synthetic Data for Classification of Small Parts
Advisor
Sajjad Taheri
Author
Amr Abdelraouf
Date
15.03.2018
Studying methods to create datasets from 3D models and use them in training a model for small parts classifier. This method will be compared with the normal manually photographing approach in terms of usability of dataset creation and performance of the classifiers.
                           
Bachelor's Thesis
Automatic Damage Detection on Metallic Surfaces
Advisor
Sajjad Taheri
Author
Leo Maximilian Vinzenz
Date
15.11.2019
Studying different methods to visually detect the damages on metallic surfaces, from simple background subtraction to supervised and unsupervised machine learning approaches, and performing an experiment to compare the results. 
   
Master's Thesis
Comparison between Cloud-based and Offline Speech Recognition Systems
Advisor
Sajjad Taheri
Author
Elma Gazetic
Date
15.04.2017
Study the popular offline open-source speech recognition systems and training/tuning them in order to compare with the cloud-based solutions
            
Master's Thesis
LeSRec: Using the Asymmetric Weight Allocation for a Learner Speech Recognition System
Advisor
Sajjad Taheri
Author
Gopala Krishna Char Cheidu Raghavendrachar
Date
15.11.2017
Implementing a Speech Recognition System, which can learn from the user input. The idea is to build an application to be able to monitor the system performance and give feedback regarding the recognized phrases. The samples, together with the corrected labels, will be used to retrain the model to improve the performance.
 
Bachelor's Thesis
Providing Training Dataset for Automatic Recognition of Small Parts
Advisor
Sajjad Taheri
Author
Jonas Pfab
Date
15.03.2017
Study the current approaches to get the right dataset for deep learning processes and implementation of an application for automatic data augmentation to enrich the training dataset